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Exploring linkages between manufacturing strategy, business

strategy, and organizational strategy

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PRODUCTION AND OPERATIONS MANAGEMENT Vol. 7, No. 3, Fall 1998

Printed in U.S.A.

EXPLORING LINKAGES BETWEEN MANUFACTURING STRATEGY, BUSINESS STRATEGY, AND

ORGANIZATIONAL STRATEGY *

YASH P. GUPTA AND SUBHASH C. LONIAL College of Business and Administration, University of Colorado at Denver,

Denver, Colorado 80217-3364, USA College of Business and Public Administration, University of Louisville,

Louisville, Kentucky 40292, USA

In recent years there has been an increased emphasis on the development of manufacturing and business strategies. In spite of that high level of emphasis, limited empirical research has been published on the linkage between manufacturing strategy, business strategy, and organizational performance. Our study examines that linkage. Our main contribution lies in (i) building con- structs for some important elements of manufacturing strategy and business strategy and (ii) testing the impact of linkage between manufacturing strategy and business strategy on organiza- tional performance. The study is based on 175 responses from senior executives in manufacturing organizations. (MANUFACTURING STRATEGY; BUSINESS STRATEGY; ORGANIZATIONAL PER- FORMANCE; LISREL MODEL)

1. Introduction

During the past several years, American manufacturing organizations have faced in- creasing challenges from international competition. For many, meeting these challenges is a matter of survival. In spite of massive investments in plant and equipment, manufac- turing industries continue to lose ground to foreign competition (Krafcik 1988; Lindberg 1990).

Since Skinner’s ( 1969) landmark article, a number of researchers have depicted the manufacturing function as the missing link in corporate strategic processes and empha- sized that manufacturing can be a formidable competitive weapon if equipped and man- aged properly (see Hayes and Wheelwright 1984; Hayes and Pisano 1995; Hill 1989; Manufacturing Studies Board 1986; Miller and Roth 1994).

Manufacturing strategy falls within the widely accepted hierarchy of strategy (Fine and Hax 1985; Hofer and Schendel 1978). This hierarchy spans three levels: corporate-level strategy, business-level strategy, and functional-level strategy. Corporate-level strategy formulation in conglomerate diversified firms is mainly characterized by the consideration of scope and resource deployments. At the business level, the scope and boundaries of each business unit (SBU) and the operational links with corporate strategy are specified.

* Received July 1994; revised August 1997; accepted October 1997. 243

1059-1478/98/0703/000$1.25 Copyright 0 1998, production and Operations Management Society

244 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

This level also establishes the basis on which the business unit will achieve and maintain a competitive advantage within its industry. At the functional-level (e.g., marketing strat- egy, manufacturing strategy, and research and development (R&D) strategy), the objec- tives are to support the desired business level strategy in a manner that will provide a competitive advantage and to determine how the functional-level strategies will comple- ment each other.

Miller and Roth ( 1994) suggested that two core elements are central to the definition of manufacturing strategy: manufacturing task, capabilities the manufacturing units must have in order for the firm to compete given its overall business and marketing strategy; and pattern of manufacturing choices. Hill ( 1989) argued that a strong link relates between manufacturing tasks and customer needs. Manufacturing tasks were defined as those ca- pabilities that are critical to winning customer orders. A number of authors have argued that a central theme links the two elements of a manufacturing strategy: the notion that the pattern of choices followed by manufacturing must be congruent with the manufac- turing task (Anderson, Cleveland, and Schroder 1989). As explained in detail later in this paper, Kotha and Ome (1989) argued that manufacturing structures (product-line com- plexity, process-structure complexity, and organizational scope) also implicitly represent generic manufacturing strategies. Consequently, we will refer to these structures as man- ufacturing strategies.

Despite the conceptual support for a strategic role for manufacturing, to date the em- pirical research, in the area of manufacturing strategy is disappointing and lacks validity (Anderson, Cleveland, and Schroder 1989; Ward, Leong, and Boyer 1994). The virtues and risks of empirical research in manufacturing strategy have been enumerated, and several reasons explain the hesitancy on the part of many operations management re- searchers to attempt empirical research (Flynn et al. 1990). The situation on the industrial front is just as disappointing. Ettlie and Penner-Hahn (1990) claim that “only about 10% of North American firms have a manufacturing strategy.”

Several researchers have examined the relationship between (i) manufacturing com- petitive priorities and their relationship to each other (Giffi, Roth, and Seal 1990), (ii) flexibility ratios and manufacturing strategy (Ettlie and Penner-Hahn 1994)) (iii) manu- facturing flexibility and business strategy (Gupta and Somers 1996)) (iv) manufacturing strategy and business origin of new venture firms (McDougall, Deane, and D’Souza 1992)) (v) competitive business strategy and technology management content and process (Lefebvre, Langley, Harvey, and Lefebvre 1992)) and manufacturing strategy and organ- izational culture (Bates, Amundson, Schroder, and Morris 1995 ) . The relationship be- tween a firm’s performance and the linkage between its operations and business strategies has been subjected to limited empirical scrutiny (Skinner 1985; Swamidass and Newell 1987; Williams, D’Souza, Rosenfeldt, and Kassaee 1995).

This paper has two purposes: (i) to establish a linkage between some aspects of man- ufacturing strategy with some aspects of business strategy and (ii) to study the impact of this linkage on an organization’s performance. We hope that the results of this study will allow senior management to better understand these inherent relationships. For research- ers, this study may provide a stepping stone toward examining the impact of this consis- tency of relationships between business strategy and other functional strategies, such as marketing strategy or R&D strategy on organizational performance.

2. Background

The literature reviewed in this section indicates that those organizations which link manufacturing strategies with business strategies may outperform those in which the two are mismatched.

Several authors have argued that an organization should develop a manufacturing strategy which is consistent with and linked to its business strategy (e.g., see Banks and

YASH P. GUPTA AND SUBHASH C. LONIAL 245

Wheelwright 1979; Cleveland 1986; Hill 1989; Lefebvre, Langley, Harvey, and Lefebvre 1992; Roman0 1983; Schmenner 1979).

Skinner ( 1974) provided a concept called “focused factory” that follows almost in- evitably from the essential idea that “each strategy creates a unique manufacturing task.” He argued that a single factory, even one ,equipped with the most modern machinery and systems, will inevitably experience irreconcilable inconsistencies and loss of overall ef- fectiveness, if it attempts to serve multiple markets while using a different competitive strategy for each market. He further suggested that narrowing the range of demands placed on a manufacturing facility will lead to better performance because management can concentrate its attention on a few key tasks and priorities.

Hayes and Wheelwright ( 1984) recommended that one of the criteria for evaluating a manufacturing strategy of an organization should be its consistency with the overall busi- ness strategy. Hayes and Schmenner ( 1978) posited that manufacturing functions best when an organization’s facilities, technology, and policies are consistent with the recog- nized priorities of its corporate strategy. After Skinner (1985) analyzed companies in several industries using the case study approach, he concluded that if an organization’s manufacturing policy is consistent with and supports the competitive strategy, it becomes a competitive weapon. If, on the other hand, the policy is not consistent with the corporate strategy, a negative influence on the company’s performance may result.

Cleveland, Schroeder, and Anderson (1989) used a sample of six manufacturers to examine the relationships among business strategy, production process, production com- petence, and business performance. Production competence, defined as a variable rather than a fixed attribute, was measured in terms of how well manufacturing strengths and weaknesses complemented the priorities of the business strategy. The study showed that production competence is linked with business performance. Vickery ( 1991) questioned Cleveland, Schroeder, and Anderson’s measure of production competence and the validity of their manufacturing competitive priorities. She introduced an improved theoretical framework for production competence and concluded that production competence is a potential valuable construct for understanding manufacturing’s contribution to business performance. Vickery, Droge, and Markland (1993) studied the impact of business strategy on performance in the presence of production competence as a moderating vari- able They found that production competence may have more of an effect on business performance for the pure strategy of differentiation, compared with strategy of differen- tiation/cost.

Using data gathered from 35 manufacturers, Swamidass and Newell ( 1987) studied the relationships among environmental uncertainty, manufacturing strategy, and business performance. To them manufacturing strategy consists of two types of variables: (i) con- tent variables, such as flexibility, and (ii) process variables, such as the role of manufac- turing managers in strategic decision making ( RMMSDM) .

A study by Richardson, Taylor, and Gordon ( 1985) provided further evidence of the need to better understand the linkage between corporate strategy and manufacturing strat- egy. A field study of 64 Canadian electronics companies led the researchers to conclude that companies with a strong match between their business mission and their manufac- turing tasks are more profitable. They argued that the way a firm competes in its markets is a key element in determining the corporate mission. The performance of a firm will be suboptimal unless it achieves the proper congruence between corporate mission and man- ufacturing strategy.

Schroeder, Anderson, and Cleveland ( 1986) advocated that a manufacturing strategy is a process which uses business strategy to determine manufacturing mission and dis- tinctive competence. By interacting between adjacent levels of the strategy hierarchy, this process determines manufacturing objectives and leads to the formulation of manufactur- ing policies.

246 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

Swamidass ( 1986) attempted to measure the perceptions of CEOS and manufacturing managers regarding the contribution of their manufacturing strategy to the overall cor- porate strategy. He concluded that the decisions of manufacturing managers, if pursued without regard for the strategic inclinations of the CEOS, may contribute very little to a firm’s business strategy and that, in some cases, their decisions could work against the business strategy. To eliminate this mismatch of priorities between manufacturing man- agers and CEOS, an organization must establish a link between its manufacturing and business strategies.

McDougal ( 1986) focused on the organizational conditions which may account for weak linkages between business strategy and manufacturing strategy. These conditions relate to the recent phenomena of divisionalizing conglomerate manufacturing organiza- tions. In these organizations, the highest level of manufacturing managers report to the divisional-level management rather than corporate management, thereby excluding man- ufacturing managers from the corporate strategy-making processes.

Ward, Leong, and Boyer (1994) identified two major dimensions of manufacturing proactiveness: (i) the degree of manufacturing’s involvement in the strategic processes of the business unit and (ii) the degree of commitment to a long-term program of in- vestments in manufacturing structure and infrastructure. Further, they demonstrated that investments in structural programs, coupled with either high levels of manufacturing in- volvement in strategic processes or planned investments in infrastructural programs, lead to higher-than-average organizational performance.

Basing their assertions on a field study of 269 firms in the manufacturing industry, Gupta and Somers ( 1996) suggested that a business strategy contributes both directly and indirectly to organizational performance. The findings provide evidence of direct effects of (i) business strategy on manufacturing flexibility (a dimension of manufacturing strat- egy ) , and (ii) manufacturing flexibility on organizational performance.

2.1. constructs

In this section, we will describe the constructs used in our study. However, rather than considering all dimensions of business and manufacturing strategies we focus only on most important aspects of them. First we identify the three dimensions of manufacturing strategy, then the relationships among the variables.

MANUFACTURING STRATEGY. The manufacturing strategy construct used in this paper has three dimensions: (i) process-structure complexity, (ii) product-line complexity, and (iii) organizational scope.

Process-structure complexity. The process-structure dimension has been a composite measure of three underlying variables: (i) the mechanization level, (ii) the systemization level, and (iii) the interconnection level. Chiantella ( 1982) described the level of mech- anization to be one of four categories: (i) manual, where a human operator performs an operation manually with a minimum of tools; (ii) machine, in which the operator seeks mechanical assistance in performing the operation; (iii) fixed program, where a fixed program machine may employ pneumatic logic, mechanical sequencing, or numerical to execute a sequence of operations; or (iv) programmable control, where a machine may execute a sequence of operations and compensate for exceptions that may occur. Chian- tella (1982) employed six levels of systemization to control the process. These levels vary from taking data from the process (recording past occurrences) to closing the loop and using the data obtained to direct the process.

The level of interconnection or integration between various process operations is gen- erally measured by several subordinate factors, such as discontinuities in the material flow, technological interdependence in various stages of the production system, and op- erational flexibility associated with the mix of lead times throughout the factory and

YASH P. GUPTA AND SUBHASH C. LONIAL 247

frequency of change in the production schedules. In this paper, we employed the level of interconnection dimension in gauging the process-structure complexity.

A production process that has declining work in process (WIP) inventories and few discontinuities in the material-process flow tends to have a greater need for integration between process stages. This condition, in turn, leads to a greater process-structure com- plexity. Business units that have high linear material flows (“product focus” for the facility layout) tend to have a higher level of process-structure complexity as compared to those with process focus for the facility layout. Similarly, very high levels of production standards in a business unit, such as large-scale automotive assembly, tend to need sig- nificantly more integration between process stages than the business units with few pro- duction standards. The control of materials flow also affects the process-structure com- piexity. When machines control the pacing of materials, different stages in the production process tend to be more interdependent as compared to the situation where an individual operator controls the pacing of materials flow. As the interdependence increases, there is an inherently greater need for integration between the process stages.

In summary, high levels of process-structure complexity imply very few discontinuities and WIP inventories, highly linear material flow (product focus for the facilities layout), high levels of machine pacing of the materials flow, and very high levels of production standards. Low levels of process-structure complexity, on the other hand, suggest numerous disconti- nuities and wrn, a process focus for the facility layout, few production standards, and individual operator control of material flow pacing. Accordingly, our study measures process-structure complexity by four variables: (i) focus on elimination of discontinuities and WIP inventories, (ii) focus of production facility layout, (iii) level of production standards, and (iv) control of materials flow. Actual questions appear in Appendix A.

Product-line complexity. Kotha and Orne argued that low product-line complexity represents productive units with short, simple product lines and high volumes of mature individual products. High product-line complexity, on the other hand, represents produc- tion units with wide product lines, complex in design, and relatively low volumes of individual products. Milk bottling plants and fast food “mini factories” (i.e., Mc- Donald’s) are good examples of low product-line complexity. However, General Motors’ Saturn plant, with its high variety of car models and many custom features, and the Boeing 747 plant both exemplify high product-line complexity.

The increase in end-product maturity is generally related to an increase in organizational experience with the product, a reduction in the number of different end products, and an increase in individual product volume. Assuming all other factors remain constant, an in- crease in organizational experience with the products in its product line tends to decrease the effective complexity of the product line. As the individual product volume decreases, the frequency of production changeovers and the variety of final products tend to increase. Individual product volumes, therefore, tend to be inversely related to product-line com- plexity. Product-line complexity is also affected by the complexity of the end products. For example, in an absolute sense, a Boeing 777 airplane is much more complex than a Toro lawnmower. Similarly, in relative terms, a Lexus ES300 car is more complex than a basic Ford Escort.

An increase in the variety of final products tends to increase the complexity of product line. For example, International Paper in New York produces large volumes of basically one type of photocopy paper, a product of low complexity. On the other hand, Mohawk Paper Co. in New York produces thousands of different, specialty papers in low volume, a more complex product line (Kotha and Ome 1989).

A high product-line complexity means a large variety of final products, low levels of product maturity, and small volumes on specific final products. A low product-line com- plexity, on the other hand, implies low variety of final products, high levels of product maturity, and high volumes on specific final products.

248 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

Based on the above discussion, we gauged product-line complexity using four items: (i) individual product volumes, (ii) the maturity of the end products produced, (iii) the degree of end product complexity, and (iv) the variety of final products.

Organizational scope. The third dimension of manufacturing strategy was given by Kotha and Orne ( 1989) as organizational scope. They argued that this dimension is nec- essary for several reasons: (i) an organization needs to process logistical and distribution information and (ii) the two preceding dimensions of process structure and product com- plexity do not address many critical structural characteristics of a manufacturing system. Therefore, the organizational scope dimension adds to the description of the manufactur- ing strategy.

As manufacturing plants expand in terms of their geographic scope, they increase the organizational scope of the business unit. Similarly, the organizational scope of a business unit tends to increase with an increase in the geographic market focus of its marketing efforts. For example, Nissan-Datsun has global organizational scope because it has man- ufacturing and marketing operations in most countries. Mercedes-Benz, on the other hand, has national scope because it has both fabrication and assembly in one country (i.e., the United States, Germany, and Japan).

The organizational scope of a business unit tends to enlarge with the increase in the number of customers, markets, and distribution channels served by the business unit (i.e., customer-market scope). The level of responsiveness may differ in areas, such as order entry, inventory service, and shopfloor scheduling needed by each of these segments. Similarly, the organizational scope also tends to increase with the increase in the absolute scale (or volume) of a business unit’s operations.

Based on the above discussion, we gauged the organizational scope as a composite measure of four variables: (i) geographic manufacturing scope, (ii) geographic market focus, (iii) customer-market scope, and (iv) scale.

MANUFACTURING STRATEGY AND THE RELATIONSHIPS AMONG ITS VARIABLES. We rec- ognize that the constructs of product-line complexity and organizational scope may not be the sole responsibility of manufacturing. Product-line complexity, in part, is derived from the marketing strategy of an organization. Hayes and Wheelwright ( 1979)) however, argued that many of the characteristics of productive units are a function of two primary dimensions, process structure and product structure. The relationship between these two could be used to explore some of the strategic options from a manufacturing perspective. They argued that a product passes through a process life cycle and that linking its process life cycle to its product life cycle forms a product-process matrix. Hayes and Wheelwright (1979) categorized manufacturing processes as jumbled flow (job shop), disconnected line flow (batch), connected line flow (assembly line), and continuous flow. As the organization evolves from job-shop to assembly-line production systems, (i) production standards are more frequently established, (ii) there are relatively fewer discontinuities in WIP inventories, (iii) the facility layout moves from process focus to product focus, and (iv) machines tend to control the flow of materials as compared to manual control in job shops.

Historically, as an industry tends to mature, most companies in that industry tend to move down the diagonal (product-process matrix) to more integrated processes with more commodity type products with relatively high volume. Conversely, complex, highly-in- tegrated, continuous flow process structures are incompatible with wide complex product lines, each with relatively low volume on individual products. Thus, a band around the diagonal represents a natural “match” between product and process structure. Positions off the diagonal band represent a “mismatch” between the product structure and the process structure. Day ( 198 1) and Thorelli and Burnett ( 198 1) suggested that this product- process matrix provides a tool for developing a corporate strategy that capitalizes on a

YASH P. GUPTA AND SUBHASH C. LONIAL 249

firm’s basic marketing and manufacturing strengths. Further, support for including prod- uct-line complexity and organizational scope was provided by Miller and Roth ( 1994). They proposed a numerical taxonomy of manufacturing strategies by arguing that the basis for defining the manufacturing strategy of a firm must come from those capabilities that a manufacturing unit must have for the firm to compete, given its overall business and marketing strategy. They suggested that manufacturing strategy groups could differ along two main dimensions: (i) the ability of the firms to differentiate themselves from competition with their product and services and (ii) the scope of their product lines and markets. Kotha and Orne (1989) noted that low process-structure complexity is compat- ible with low product-line complexity and vice-versa.

Business strategy and its relationship with the manufacturing strategy constructs. Re- cently, several frameworks incorporating the notion of business strategy have been proposed (for example, see Henderson 1970; Buzzell, Gale, and Sultan 1975; Hofer and Schendel 1978; Wissema, van der Pol, and Messer 1980; Miles and Snow 1978). White (1986) argued in favor of using Porter’s strategies by suggesting that “it makes sense to proceed by selecting a simple business strategy concept which incorporates a few critical dimensions yet has strong theoretical underpinnings.” Similar arguments for the use of Porter’s typology have been made by Smith and Grimm ( 1987). Therefore, in this paper, we have operationalized business strategy by using Porter’s typology.

According to Porter’s ( 1980) framework, effective business strategies can be classified as either cost-leadership strategies or differentiation strategies. However, for organizations using advanced manufacturing systems, such as flexible manufacturing systems, Porter’s strategy typology may not be as clearly applicable as under traditional manufacturing systems (see Doll and Vanderembse 1987, for fuller discussion on this issue).

Cost leadership enables the firm to attain a competitive advantage through the achieve- ment of operating efficiencies and economies of scale. Cost-leadership strategies normally entail the use of long production runs, few product variations, and few machine set-ups, resulting in lower associated down-time. Low product-line complexity is, therefore, likely to be compatible with cost-leadership strategies. Increased product-line complexity, on the other hand, implies a large variety and a small volume of final products. These products generally are at low levels of product maturity. Therefore, high product-line complexity is likely to be incompatible with cost-leadership strategies.

Cost-leadership strategies also require a strong focus on cost reduction and control by eliminating discontinuity in the production process and WIP inventories. This streamlining of the production process is generally achieved through two approaches: (i) automating the production lines via hard-tooling and (ii) employing relatively less skilled workers while imposing rigid production standards. This usually entails high levels of process structure that are supported by a product-focus type of facility layout and few component redundancies and alternatives in material flow. Therefore, high levels of process-structure complexity are likely to be consistent with the pursuit of cost leadership strategies.

Cost or price sensitivity alone, however, cannot sustain cost-leadership strategy. Certain characteristics of the supplier industry can provide a durable basis for this strategy. Cost advantage may be derived by backward integration when the supplier industry enjoys considerable economies of scale and when the entry barriers are high. Depending on industry structure, cost advantage can also be gained from preferential access to distribution channels (Williamson 198 1) . Forward integration can provide cost advantage by capturing the best locations for distribution, leaving the less desir- able sites for the competitors. Cost-leadership strategy can be sustained when the process technologies employed in the value chain indicate that significant innovation can continuously be realized, thus requiring the organization to focus on process en- gineering skills.

250 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

In an organization which can achieve greater scales of operations than its competitors and where the size of the market is limited, a focus on gaining a relative dominant share of the market may lead to cost advantage.

Differentiation strategies refer to the creation of something that is unique to the industry. This uniqueness “provides insulation against competitive rivalry because of brand loyalty by customers and resultant lower sensitivity to price” (Porter 1980, p. 38).

Differentiation strategies are characterized by more product variations, shorter produc- tion runs, more machine set-ups, and higher associated down-time. Therefore, high prod- uct-line complexity is likely to be compatible with differentiation strategies. On the other hand, increased product-line complexity is characterized by a large variety and a small volume of final products that are likely to be at high levels of product maturity. Conse- quently, low product-line complexity is likely to be incompatible with differentiation strategies. Differentiation usually entails low levels of process structure and high levels of flexibility in the production process. This low level of process structure is normally consistent with higher levels of component redundancy and alternatives in material flow. Therefore, high levels of process structure complexity are likely to be inconsistent with the pursuit of differentiation strategies.

If customers do not value products that differ along nonprice dimensions, they are not likely to value a differentiated product and generally are not willing to pay more for it. Differentiation strategy, therefore, is viable only if customers give weight to product attributes other than price when making a purchase decision (Murray 1988). As the market matures, however, the ability to sustain differentiation solely on the basis of product attributes may not be possible. Quality and service have recently received much attention to enhance differentiation. An organization can also improve the viability of differentiation strategy by focusing on product engineering skills so that product innovations can be realized on a continuous fashion.

Business strategy was measured by eight items provided by Kotha and Orne (1989). These items were measured using five-point Likert-type scales. Scale items included a focus on cost reduction and cost control, the level of process engineering skills, a focus on products and services designed for premium value, the level of product engineering skills, the level of flexibility in production scheduling, the relative market share, the level of vertical integration, and the variety of customers and market and channel segments.

Organizational pe$ormance. Several authors have emphasized the importance of the performance concept and have provided prescriptions for improving organizational per- formance (for example, see Nash 1983) ; nevertheless, the debate on issues of terminology, levels of analysis, and conceptual bases for assessment of performance continues to rage in the academic community (Ford and Schellenberg 1982). Some researchers have ex- pressed extreme frustration with the lack of agreement on basic terminology and definition (Kanter and Brinkerhoff 1981). Venkatraman and Ramanujam ( 1986) suggested that business performance is a subset of the overall concept of organizational effectiveness. They argued that in its narrowest sense business performance is associated with simple outcome-based financial indicators (referred to as financial performance) “that are as- sumed to reflect the fulfillment of the economic goals of the firm.” ,Financial performance has been most widely used to determine the organizational health of a firm. Typical indicators associated with this approach include returns on investment, returns on sales, and returns on equity.

A broader conceptualization of business performance includes emphases on indicators of operational performance (i.e., nonfinancial) in addition to financial indicators. Under this conceptualization, measures such as market share, new product introductions, product quality, and market effectiveness might be considered within the domain of business performance. According to Venkatraman and Ramanujam ( 1986, p. 804), “the inclusion of performance indicators takes us beyond the black-box approach that seems to charac-

YASH P. GUPTA AND SUBHASH C. LONIAL 251

terize the exclusive use of financial indicators and focuses on those key operational success factors that might lead to financial performance.”

The sources of data to measure financial performance, operational performance, or both can be primary (e.g., data collected directly from organizations) or secondary (e.g., data from publicly available records). Venkatraman and Ramanujam ( 1987) posited that “in the past a general belief existed that perceptual data on organizational performance may not be valid because managers are likely to overrate their organization’s performance.” However, their study of 207 senior-level managers showed. that managers tend to be less biased in their assessments of their organizational performance than researchers expect. They argued that the perceptual data gathered from managers can be employed as ac- ceptable operationalizations of organizational performance. Similarly, they showed that a positive and statistically significant association exists between primary and secondary business performance data. Moreover, precedents in the literature show use of similarly worded scales (for example, see Venkatraman 1989).

Accordingly, in this study we used only primary sources to collect data on 12 dimen- sions of performance suggested by Gupta and Govindarajan ( 1984): sales-growth rate, market share, operating profits, profit-to-sales ratio, cash-flow from operations, return on investment, new product development, market development, R&D activities, cost-reduction programs, personnel development, and political/public affairs. For each of the above dimensions, respondents rated their business unit’s performance relative to their major competitors on a five-point Likert-type scale, ranging from 1 (much worse than compe- tition) to 5 (much better than competition). We computed the performance index for each business unit by summing the response scores on all the performance dimensions. Re- cently, in the operations management field, Gupta and Somers ( 1996) have used the same self-rated measures to gauge organizational performance.

2.2. Hypothesis

Kotha and Orne ( 1989) proposed three dimensions of manufacturing strategy which provide a basis for examining the following relationships:

H, : Business strategy directly affects organizational performance. Hz : Manufacturing strategy directly affects organizational performance. H3 : Business strategy also affects organizational performance through manufacturing

strategy.

3. Methodology

This section describes the research methodology. The item selection and measurement is addressed first, followed by data collection and operationalization of the constructs.

3.1. Item Selection and Measurement

The measurements used for both manufacturing and business strategies were developed by Kotha and Orne. Organizational performance variables were provided by Gupta and Govindarajan ( 1984). These measures appear in Appendix A.

3.2. Data Collection

The research instrument was designed and pretested in consultation with three acade- micians, four CEOS of manufacturing organizations, and three management consultants whose collective experience extended across a broad range of manufacturing organiza- tions. A copy of the final version of the questionnaire was mailed to 800 CEOS and pres- idents of manufacturing organizations. The target organizations were randomly selected from a list of 4,000 medium-to-large manufacturing organizations in the Midwest region of the United States. The list was provided by Manufacturers’ News Incorporated, the

252 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

publishers of manufacturers’ registers for various states. The postal service returned 22 of the 800 questionnaires marked as undeliverable. Fifteen of the firms returned letters indicating their unwillingness to participate. One hundred and ninety-six organizations returned the questionnaires; however, only 175 questionnaires could be used for our anal- ysis because of missing data. These 175 usable questionnaires represent a 22% response rate, which is considered to be adequate for studies where senior executives are the re- spondents. Chi-square tests revealed no significant differences between responding and nonresponding organizations in terms of sales and number of employees. However, it is conceivable that these differences existed in terms of industry type, rank of respon- dent, etc.

Our research used the “Total Design Method” proposed by Dillman ( 1978). The initial mailing included (i) a cover letter explaining the purpose of the study, (ii) an unmarked questionnaire, and (iii) a business reply envelope. As a modification of the Dillman meth- odology, a return post card containing the respondent’s name and address was also in; eluded in the first mailing, and respondents were asked to return the post card and ques- tionnaire separately. The purpose of this deviation was to provide the respondents with the assurance of confidentiality while enabling the researchers to maintain an accurate follow-up list. The second mailing consisted of a reminder post card which requested respondents to complete and return their questionnaires if they had not already done so. The third mailing consisted of the second wave of questionnaires along with cover letters and return envelopes.

Tables l-3 represent the demographics of the manufacturing organizations. The ma- jority of the responding companies employed 250-5,000 people and sold $25 million to

TABLE 1

Distribution of the Sample According to the Type of Business

Type of Business Percentage

Precision machine 2.4 Electrical & electronic 8.6 Industrial machine 6.7 Metal products 20.1 Automobile 13.9 Transportation 1.0 Petro chemical 2.9 Other 42.6 No response 1.9 Total 100.0

TABLE 2

Distribution of Number of Employees of the Sample Firms

Number Percentage

10-250 9.2 51-500 27.3

sol-loo0 25.4 1001-5000 28.7 .5001- 10,000 2.4 over 10,000 5.7 No response 1.4 Total 100.0

YASH P. GUPTA AND SUBHASH C. LONIAL 253

TABLE 3

Distribution of Annual Sales of the Sample Firms (Millions)

Sales Percentage

<$25 $25~$50 $51-$100

$lOl-$250 $251-$500 $501-$1,000 >$l,OOO No response Total

10.1 20.1 21.1 18.2 14.4 5.1

10.0 0.5

100.0

$500 million worth of goods. The respondents had been in their current positions an average of 5.3 years and had 22.7 years of experience. Their titles were as follows: Chief Executive Officer/President/Chairman of the Board/Chief Operating Officer/General Manager (52.2%)) Executive Vice-President ( 17.4%)) Vice-President, Operations ( 16.8%), and Vice-President, Marketing ( 13.6%). The respondents’ educational quali- fications were as follows: high school (3.6%), undergraduate degree (51.5%)) Master’s degree (37.2%), and doctorate (7.6%).

A likelihood of bias in our data stem from the fact that we used a single informant in our study. Asking a single informant to make judgments about organizational character- istics may increase the subjective propensity of respondents to seek out consistency in their responses and increase random measurement error (Miller and Roth 1994). Phillips ( 198 1) , however, argued that high ranking respondents tend to be more reliable sources of information than their lower ranking counterparts. The high rank of the respondents in our study helps to moderate the mono-respondent problem.

3.3. Analytical Method

We analyzed the data using the LISREL statistical package (Joreskog and Sorbom 1989), a tool for estimating causal effects adjusted for measurement error, examining correlated residuals, and obtaining indicators of a model’s fit to data. One of the advan- tages of using LISREL over more traditional methods, like multiple regression, is that it permits specification of latent manifest variables with explicit modeling of measurement errors. Latent variables often represent theoretical concepts, and manifest variables usually consist of multiple empirical measures for each concept.

3.4. Operationalization of Constructs

In this section, we will discuss the operationalization of the constructs of business strategy, manufacturing strategy, and organizational performance. To operationalize the constructs, the following properties of the measures are considered minimally important (Bagozzi and Phillips 1982; Venkatraman and Grant 1986): internal consistency of op- erationalization (i.e., reliability and unidimensionality ) , convergent validity, and discrim- inant validity. Internal consistency refers to two broad issues: reliability and unidimen- sionality. Assessing unidimensionality ensures that all the items measure the underlying theoretical construct of interest, whereas reliability indicates the degree to which measures are free from random error and, thus, yield consistent results. Convergent validity assesses the consistency in measurements across multiple operationalizations, whereas discriminant validity demonstrates when a measure does not correlate very highly with another measure from which it should differ.

254 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

TABLE 4 Initial Conjrmatory Factor Analysis Results

Dimension Number of Indicators Chi-Square df p Value

Adjusted Goodness-of-Fit

Index

Performance 12 326.59 54 0.0 ,617 Bus. strategy 8 44.88 20 0.001 ,911 Mfg. strategy 12 126.64 51 0.00 ,862

The data obtained were subjected to confirmatory factor analysis ( CFA) (i.e., the method in which precise specifications concerning the factor structure are provided a priori) as opposed to exploratory factor analysis ( EFA) . We took this approach because we wanted to test a priori the theoretically specified dimensions of manufacturing strategy, business strategy, and organizational performance. Mulaik ( 1972) provides a strong argument in favor of performing confirmatory factor analysis by suggesting that the major disadvantage of pure EFA lies in the difficulty involved in interpreting the factors. Implementing the CFA method within the LISREL framework “allows the specification of measurement errors within a broader context of assessing measurement properties and describes a causal indicator model where the operational indicators are reflective of the unobserved theo- retical construct” (Venkatraman 1989, p. 952).

BUSINESS STRATEGY. Eight items defining the business strategy were also subjected to CFA (Table 4). The initial adjusted goodness-of-fit index was 0.9 11. The Chi-square with 20 degrees of freedom was 44.88 (p = 0.001). All t values were higher than 2.5. Since the p value was less than the acceptable level of 0.05, three items with low correlation were dropped after three iterations of the analysis. The remaining five items (marked with asterisks and shown in Appendix A) were again subjected to CFA. Table 5 shows the adjusted goodness-of-fit index is equal to 0.953. Chi-square value with 5 degrees of free- dom = 8.31 (p = 0.14). All t values were greater than 3.4. These values represent a reasonably good fit.

The high dropout rate for items within the above constructs results from the fact that we are in a more exploratory stage of research and that an important part of what we are contributing is the development of scales. Thus, we would expect that not all measured variables go into the construct as initially hypothesized. Moreover, precedents in the literature identify cases where the dropout rate of items within the constructs was signif- icantly higher than in our study (for example, see Doll and Torkzadeh 1988).

ORGANIZATIONAL PERFORMANCE. Twelve items indicating organizational performance were subjected to confirmatory factor analysis. The results did not produce a good fit (see Table 4). Seven items which had low reliability were dropped from further analysis

TABLE 5 Final Co&matory Factor Analysis Results

Dimension Indicators

Performance 5 Bus. strategy 5 Mfg. strategy 9

Chi-Square df

5.86 5 8.31 5

33.38 24

p Value

0.320 0.14 0.096

Adjusted Goodness-of-Fit

Index

0.966 0.953 0.938

YASH P. GUPTA AND SUBHASH C. LONIAL 255

through various iterations of CFA. Finally, five items with asterisks (shown in Appendix A) were retained resulting in an acceptable degree of fit (shown in Table 5 ) . The t values of all five remaining variables were greater than 6.0. The adjusted goodness-of-fit index for the organizational performance construct is 0.966 and chi-square value with five de- grees of freedom is 5.86 (p = 0.32) (Table 5).

MANUFACTURING STRATEGY. Manufacturing strategy was measured using three di- mensions: product-line complexity, process-structure complexity, and organizational scope. First, each dimension was subjected to CFA. The results indicated that, to improve the fit, one item from each of the three dimensions must be dropped. The final analysis indicated that each dimension could be represented by three items. Appendix A identifies those items retained as the result of achieving an acceptable degree of fit (marked with an asterisk) in terms of the adjusted goodness-of-fit index, degrees of freedom, and p values. Next, these three individual constructs of manufacturing strategy were subjected again to confirmatory factor analysis to determine if these nine variables collectively represented the construct of manufacturing strategy. Table 5 indicates that manufacturing strategy with a chi-square value of 33.38 and degree of freedom of 24 has a p value of 0.096 with an adjusted goodness-of-fit index of 0.938. Table 6 shows the t values for modified and improved fit of manufacturing strategy; this table shows that all the t values are greater than two, which is considered within acceptable limits.

To determine unidimensionality and convergent validity of manufacturing strategy as a construct, we computed Bentler and Bonett’s (1980) incremental fit index, which is an indication of the practical significance of the model in explaining the data. For all three dimensions of manufacturing strategy (i.e., product-structure complexity, process-com- plexity and organizational scope), the Bentler and Bonett index was greater than 0.95. This finding suggests that each of the three dimensions had achieved unidimensionality and convergent validity (Bagozzi 1980).

The results for unidimensionality do not provide a direct assessment of construct reli- ability. Typically, we employ the Cronbach (Y coefficient as a measure of reliability; however, this coefficient is based on a restricted assumption assigning equal importance to all indicators. Werts, Linn, and Joreskog ( 1974) suggested an alternative measure ( pc) which represents the ratio of trait variance to the sum of the trait and error variances. Table 7 shows all the pc indices are greater than 0.5, which is considered to be an ac- ceptable value in establishing the internal consistency of the dimensions being studied (Bagozzi 1980).

TABLE 6 t Values for Modified and Improved Fit for Manufacturing Strategy

Variables Process Structure

Complexity Product Line Complexity

Organizational Scope

Focus on elimination of discontinuities and work-in-process inventories 8.382*

Focus of the production facility layout 7.736* Level of production standards 4.641* Level of individual product volume 0.0 Level of maturity of end-products 0.0 Degree of end-product complexity 0.0 Geographic manufacturing scope 0.0 Geographic market focus 0.0 Customer-market scope 0.0

0.0 0.0 0.0 0.0 0.0 0.0 6.636* 0.0 3.902* 0.0 6.860* 0.0 0.0 5.606* 0.0 10.34* 0.0 7.94s*

* Significantly different at p < 0.001.

256 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

TABLE I

Assessment of Internal Consistency Reliability Indices

Dimension Number of Indicators Rhoc

Process structure complexity 3 0.514 Product line complexity 3 0.532 Organizational scope 3 0.505 Business strategy 5 0.729 Performance 5 0.937

The final issue which needed to be examined was whether each of the three dimensions of manufacturing strategy were unique from each other (i.e., conduct discriminant valid- ity). According to Venkatraman ( 1989),

This is achieved when measures of each dimension converge on their corresponding true scores and can be tested that the correlations between the pairs of dimensions are significantly different from unity. This requires a comparison of a model with this correlation constrained to equal one with the unconstrained model. A significantly lower x2 value for the model with the unconstrained correlation, when compared with the constrained model, provides support for discriminant validity.

Table 8 reports the results of three pairwise tests conducted for discriminant validity. All the pairs satisfy the discriminant validity criteria. Table 9 provides the basic statistics (mean, standard deviation, maximum and minimum values) of the variables used to op- erationalize manufacturing strategy.

4. Composite Model and Results

A causal modeling approach using composites instead of multiple indicators represented the constructs and tested the hypotheses. Figure 1 shows the causal model studied in this paper. Five indicators each for business strategy and for organizational performance were collapsed, and a single composite indicator was developed for each of the constructs using Liang, Lawrence, Benett, and Whitelaw’s ( 1990) recommendations. Similarly, manufac- turing strategy was represented by three composites, one each for process-structure com- plexity, product-line complexity, and organizational scope. In the conventions of LISREL analysis, lowercase Greek letters depict parameters to be estimated, numerals indicate constrained parameters, circles represent latent or unobserved constructs, boxes refer to manifest or measured variables, arrows connecting circles stand for hypothesized causal paths, arrows between circles and boxes are measurement relations, and arrows without origin are error terms. In Figure 1, for each latent variable, the first X has been set equal

TABLE 8

Assessment of Discriminant Validity

Test # Description

Chi-squared Constrained Model (&)

Chi-squared Unconstrained

Model (df) Diff.

Process structure complexity with prod. line complexity

Process structure complexity with organizational scope

Product line complexity with organizational scope

90.79 (9) 10.32 (8) 80.47*

74.09 (9) 5.20 (8) 68.89*

60.76 (9) 9.70 (8) 51.06*

* Significantly different at p < 0.001.

YASH P. GUPTA AND SUBHASH C. LONIAL 257

TABLE 9 Basic Statistics of Manufacturing Strategy Variables

Variables Mean Value

Standard Deviation

Min. Value

Max. Value

Focus on elimination of discontinuities and work-in-process inventories 3.13 1.01

Focus of the production facility layout 2.88 1.28 Level of production standards 3.94 0.93 Level of individual product volume 2.81 1.04 Level of maturity of end-products 2.98 0.85 Degree of end-product complexity 3.58 1.10 Geographic manufacturing scope 2.99 1.16 Geographic market focus 3.20 1.17 Customer-market scope 3.15 1.43

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 4.0 4.0 5.0 5.0 5.0 5.0

to unity to fix the scales of measurement. This convention, as recommended by Joreskog and Sorbom ( 1978)) merely scales the latent variables in the same units of measurement as their respective observed operationalizations. It does not affect the test of hypotheses; rather, it aids in the interpretation of the adequacy of measurements. The rescale produces estimates for the unconstrained X’s in relation to the respective constrained A. The null hypothesis is shown in Figure 1, and the alternative hypothesis is that C is any positive definite matrix. The null model, which includes no relationships among latent variables or between the latent and manifest variables, was used as the baseline model in this study. The fit of the model was evaluated by comparing the difference in the chi-square values of the two models. Additional fit indexes provided by LISREL VII were also calculated: the adjusted goodness-of-fit indexes ( GFI) and root mean square residual. Two incremental fit indexes were also calculated: the Tucker-Lewis index ( TLI) (Tucker and Lewis 1973) and the comparative fit index (cn) (Bentler 1990). The incremental fit indexes are in- dependent of sample size and degree of freedom, in contrast to the GFI and root mean square residual, which perform poorly against the criteria of sample size and degree of

FIGURE 1. Model Under Study

258 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

freedom (Marsh, Balla, and McDonald 1988). Bentler and Bonett (1980) suggested that a value of 0.90 or higher for TLI or CFI indicates an adequate fit of model to data.

To evaluate if the model fits the data well, each of the parameter estimates (Xi, yi, and /li ) are tested to determine whether they are significantly different from zero; then the II,, : Ai = 0; yi = 0; pi = 0 are all rejected in favor of the alternative hypothesis that they are not equal to zero. The results of our LISREL analysis appear in Table 10. As can be seen in this table, a satisfactory model is achieved (i.e., the goodness-of-fit test are x2 (4) = 7.03, p =. 134 and all t values are greater than 2.0 and the values for TLI and CFI were 0.935 and 0.9, respectively). As hypothesized, one cannot reject the overall null hypothesis corresponding to Figure 1. That is, (i) business strategy directly affects organizational performance, (ii) manufacturing strategy directly affects organizational performance, and (iii) business strategy also affects organizational performance through manufacturing strategy.

The LISREL output also provides square multiple correlations for each of the observed indicators separately and coefficient of determination for all the observed variables jointly. Further, we calculated the squared multiple correlations for each of the structural equations to measure the strength of the linear relationship and the coefficient of determination, a measure of the strength of several relationships jointly (Joreskog and Sorbom 1989). The squared multiple correlation (variance explained) by manufacturing strategy and business strategy in the model is 33.8% and 34.2%, respectively. Process-structure complexity, product-line complexity, and organizational scope each explain 5 1.4%, 23.2%, and 20.5% of the variance, respectively, for manufacturing strategy. Overall, the coefficient of de- termination (strength of relationship) relating manufacturing strategy, business strategy, and performance is 0.369 (i.e., 36.9% of the variance explained). The fact that product- line complexity and organizational scope accounted for less than 24% of variance indicates that decisions being made in other functional areas account for additional variance. This result is consistent with the explanation put forward before in this paper; that is, that these two factors are not entirely under the jurisdiction of the operations function.

The parameter estimates for the measurement submodel provide information about the re- lationship of observed variables to the respective latent constructs. The values presented for the x’s are the standardized estimates and may be interpreted as the regression of the measurements on their respective latent constructs. The error terms are residuals representing the random error in measurements. In Table 10, all estimates for the h’s are high and statistically significant.

The parameter estimates for the causal paths in Table 10 are the primary hypothesized relationships shown in Figure 1. The first parameters to note are the ones relating organ- izational performance ( n2) to manufacturing strategy and business strategy (7,) I), rep-

TABLE 10

Results qf Final Composite Model

Parameter Standardized Estimate t Value

0.717 Fixed 0.482 4.828* 0.453 4.642* 0.968 Fixed 0.854 Fixed 0.380 2.853* 0.581 5.611* 0.228 2.018*

Chi-square with 4 df = 7.03 (p = 0.134). Adjusted goodness of fit index = 0.95. Root mean square residual = 0.034. Tucker-Lewis index = 0.935. Comparative fit index = 0.9.

* Significantly different at p < 0.001.

YASH P. GUPTA AND SUBHASH C. LONIAL 259

resented by & and yZ1, respectively. Both values are statistically significant, indicating that organizational performance is influenced by both business strategy and manufacturing strategy. Table 10 also shows that the value of p2r is greater than yZl, indicating that manufacturing strategy has a greater influence than business strategy on organizational performance. These results support our hypotheses (H, and H,) . The value of parameter yll is significant, suggesting that business strategy also influences manufacturing strategy. This finding implies that our data supports the hypothesis (H3): the business strategy affects organizational performance through the manufacturing strategy. It must be noted that the direct impact of business strategy on organizational performance as indicated by yzl (standard estimate = 0.228) is about the same as the impact of business strategy on organizational performance through the manufacturing strategy as indicated by &.yr,(O.380*0.581 = 0.221).

5. Discussion and Implications

This exploratory study was designed to build constructs of manufacturing strategy and to examine the linkage between manufacturing strategies, business strategies, and performance of organizations. The results clearly indicate that manufacturing strategy and business strategy independently, as well as collectively, affect organizational performance. Our results verify the observations made by several authors (for example, see Banks and Wheelwright 1979; Schmenner 1979; Wheelwright 1981; Roman0 1983; Hayes and Wheelwright 1984; Rich- ardson, Taylor, and Gordon 1985; McDougall 1986; Cleveland 1986; Hill 1989).

Development and implementation of manufacturing strategy has been cited as a critical issue for both manufacturing executives and general managers (Ettlie and Penner-Hahn 1990). The increasing belief in the importance of manufacturing in general, along with the level of executive manager participation in our study, suggest that manufacturing strategy will continue to be a critical issue well into the next decade.

In this study we have shown that manufacturing strategy directly affects organizational per- formance, implying that management must place greater emphasis on manufacturing strategy. However, manufacturing strategy should not be viewed as static. According to Hayes and Pisano ( 1995)) in a placid environment, organizational competitive strategy “is about staking out a position and manufacturing strategy focuses on getting better at the things necessary to defend that position.” In the dynamic environment we are currently facing, the role of manufacturing strategy must not solely be to align operations to current competitive priorities, but also to continuously select and create the operating capabilities a company will need in the market place in the future (Hayes and Pisano 1996; Wheelwright and Bowen 1996).

Our results also show that business strategy has a significant impact on organizational performance through manufacturing strategy. Senior management, therefore, should pay close attention to the definitions of manufacturing and business strategies to ensure their alignment. Many mechanisms to enhance this alignment have been reported in the literature. Wheelwright and Bowen ( 1996) suggested that integration between manufacturing and business strategies can be enhanced by effective leadership present within manufacturing operations, as well as the business unit. They argued that “leadership plays an essential role in establishing a vision and sense of direction, ensuring that patterns of decisions are aligned with that vision and direction, and maintaining momentum and consistency in follow- through.” It is unlikely that leadership in operations would have been cultivated or nurtured in organizations where manufacturing plays a role as externally neutral or internally sup- portive (Hayes and Wheelwright 1985)) and where other functions have been viewed as the primary provider of competitive advantage. Implementation of new manufacturing ac- counting systems discussed by Drucker ( 1990) can assist an organization in aligning its manufacturing strategy with its business strategy. New manufacturing accounting systems, for example, allow organizations to measure the impact of manufacturing changes on the

260 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

total business, the return in the marketplace of an investment in automation or the risk in not making an investment that would speed up production changeover.

In measuring manufacturing strategy, we have relied on several constructs that usually do not fall solely under the jurisdiction of manufacturing. In determining the organiza- tional scope and product-line complexity, management requires an input from several functional areas, including marketing and engineering. This breadth of coverage suggests that management must build or develop relationships between manufacturing and other functional areas and must emphasize a set of activities that link the design of products to customers as an integrative system. Although traditional manufacturing companies divide the activities into different departments, they remain highly interdependent. Failure to recognize those interactions may lead to designs that are difficult to produce, products that are late to market, and manufacturing performance far below its potential. A wealth of literature argues strongly in favor of building such relationships (for example, see Wheelwright and Bowen 1996; Crittenden 1992). Drucker ( 1990) provided an illustrative example of such collaboration. Honda decided to create a new market for its upscale Acura automobiles by building separate dealerships at high cost and risk and not use its well-established network selling Honda automobiles. This marketing issue was decided by a team of design, engineering, marketing, and manufacturing people. Drucker sug- gested that “what tilted the balance toward the separate dealer network was a manufac- turing consideration: the design for which independent distribution and service made most sense was the design that best utilized Honda’s manufacturing capabilities.”

In this study we did not control for industry (for example, aerospace and automobile) and environmental variables because the sample size would have become extremely small. Therefore, the results reported in this paper should be generalized with some caution. The statistical significance of these results, however, provides us with a reasonable level of protection against spurious and unreliable findings.

Finally, the contributions of this study fall into two categories: (i) it empirically vali- dates the observations made by several authors that the linkage between manufacturing and business strategy should lead to better business performance, and (ii) it contributes to the development of manufacturing strategy theory by adding to the existing body of literature on the subject.

6. Future Work

Subsequent researchers might extend this study by performing longitudinal analyses of organizations. A study of evolution of manufacturing and business strategies could estab- lish just exactly how their linkage affects the performance of organizations. Detailed case histories of organizations might serve to analyze more precisely the causes of maladap- tiveness in different settings. Since we did not control for the size or the type of manu- facturing company (e.g., process engineering and petroleum), it would be worthwhile to see if the findings can be replicated in different manufacturing environments and in dif- ferent industries. As suggested by Kotha and Ome ( 1989), it would also be useful to include a typology of computer integrated (CIM) technologies to explore the concept of fit between business level strategy, manufacturing structure, and choices in CIM technology (Kotha 199 1) . Finally, a study using other typologies of manufacturing strategy should be undertaken to determine whether the results (i.e., a linkage between manufacturing and business level strategies) obtained in this study can be replicated. These follow-up studies will provide more credibility in generalizing the study results.’

’ We acknowledge the constructive comments made by two reviewers and the associate editor on the earlier versions of this paper. We also appreciate the guidance provided by Professor Baggozi on data analysis.

YASH P. GUPTA AND SUBHASH C. LONIAL 261

Organizational Performance

APPENDIX A

Please indicate by circling the appropriate number your position relative to your major competitors along the following dimensions:

1. Much worse than competition 2. Worse then competition 3. Same as competition 4. Better than competition 5. Much better than competition

Sale growth rate Market share *Operating profits *Profit to sales ratio *Cash flow from operations *Return on investment New product development *Market development R&D activities Cost reduction programs Personnel development Political/public affairs

Low 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2

Business strategy

How would you describe your organization in terms of each of the following variables: *Level of engineering skills process 1 2 *Focus on cost reduction and cost control 1 2 *Focus on products and services designed for premium value 1 2 *Level of product engineering skills 1 2 Level of vertical integration 1 2 Focus on variety of customers 1 2 *Focus on relative market share 1 2 Focus on variety of customers and market and channel segments 1 2

Process structure complexity

3 3 3 3 3 3 3 3 3 3 3 3

3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4 4 4 4

4 4 4 4 4 4 4 4

High 5 5 5 5 5 5 5 5 5 5 5 5

Please indicate by circling the appropriate number the extent to which the following statements describe your business unit’s manufacturing operations. *Focus on elimination of discontinuities and work-in-process

inventories 1 2 3 4 5 *Level of production standards 1 2 3 4 5 Control of material flows 1 2 3 4 5 *Focus of production facility layout+ 1 2 3 4 5

Product line complexity

*Level of individual final product volumes 1 2 3 4 5 *Degree of end-product complexity 1 2 3 4 5 Variety of final products 1 2 3 4 5 *Level of maturity of end products produced 1 2 3 4 5

Organizational scope

*Level of geographic manufacturing scope of the business units 1 2 3 4 5 *Level of geographic market focus of the business units 1 2 3 4 5 *Level of customer-market scope of the business units 1 2 3 4 5 Level of volume of operations 1 2 3 4 5

* Items retained as the result of achieving an acceptable degree of fit. ’ Product focus to process focus for 1 through 5, respectively.

262 LINKAGES BETWEEN STRATEGIES AND ORGANIZATIONAL PERFORMANCE

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Yash Gupta is Professor and Dean of the College of Business, University of Colorado at Denver. Prior to this position, he was the Frazier Family Professor at the School of Business, University of Louisville, Louisville, Kentucky. He has also taught at the Uni- versity of Manitoba, Canada; Memorial University, Canada; and the University of Brad- ford, England. He has consulted with large international companies, financial institutions, government agencies, and not-for-profit healthcare organizations in several countries, in- clnding England, the Netherlands, Belgium, Germany, Sweden, Switzerland, Canada, and the United States. He holds a B.S. (Engineering), M.Tech., and a Ph.D. from the Uni- versity of Bradford, England. Yash’s research interests are in the areas of planning and control systems for advanced manufacturing systems, information systems strategies, and total quality management. He teaches courses in management information systems and operations management. He is the author of over 160 articles which have been published in numerous international journals. He serves on the editorial boards of four journals and has been invited to be the guest editor for several journals. He is a recipient of numerous awards of excellence from various institutions and professional organizations, including the President’s Award for Outstanding Scholarship, Research, and Creative Activity for 199 1. In 1994 and 1996 he was ranked as the most prolific scholar in the area of operations management in the United States.

Subhash C. Lonial received his Ph.D. and MBA from the University of Louisville, Louisville, Kentucky, and his B.S. from Carnegie Institute of Technology. He is Professor of marketing at the University of Loiusville. His primary teaching and research interests are in marketing and manufacturing strategy, and market segmentation. His writings among others have appeared in Decision Science, Journal of Advertising Research, Jour- nal of Advertising, Journal of Academy of Marketing Science, Manufacturing Review, Journal of Consumer Psychology, and The International Journal of Operations and Pro- duction Management.

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